Here’s a list of the major projects I’ve worked on and my key publications. You might also be interested in my presentations, my full CV, or my ORCID profile.
I currently work with Joel Greenhouse to design statistical models to predict crime by using crime hotspots, spatial features, seasonal factors, and leading indicators (like 311 calls, criminal mischief, and so on). My goal is both to improve crime prediction and to provide inference tools for criminologists to understand factors that lead to crime. I also work on evaluation and diagnostic methods to understand the performance of predictive policing models.
My dissertation work was supported by a National Institute of Justice Graduate Research Fellowship (GRF-STEM).
- Reinhart, A., & Greenhouse, J. (2018). Self-exciting point processes with spatial covariates: Modeling the dynamics of crime. Journal of the Royal Statistical Society: Series C, 67(5), 1305–1329. doi:10.1111/rssc.12277. http://arxiv.org/abs/1708.03579
- Reinhart, A. (2018). A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications. Statistical Science, 33(3), 299–318. doi:10.1214/17-STS629. http://arxiv.org/abs/1708.02647 (With invited discussion)
- Reinhart, A., & Nagin, D. S. (2017). The Next Step: A Spatiotemporal Statistical Model of the Birth and Death of Crime Hotspots. Jerusalem Review of Legal Studies, 15(1), 55–60. doi:10.1093/jrls/jlx007. https://www.refsmmat.com/files/papers/jrls.pdf
Radiation anomaly detection
As an undergraduate I started a project (supervised by Alex Athey of Applied Research Laboratories) to devise methods to continuously monitor the radiation background in a wide area and detect any sudden changes, such as might be introduced by a dirty bomb or stolen radioactive source. We built a system which uses gamma spectroscopy to compare new measurements to previous observations of the radiation background, making it feasible to monitor a wide area with mobile detectors and rapidly detect changes.
At Carnegie Mellon University, I continued the project under Valérie Ventura and Chad Schafer, proposing a new method based on Kolmogorov–Smirnov tests. James Scott and Wesley Tansey continued the work to devise a new spatial smoother for radiation spectra.
- Padilla, O. H. M., Athey, A., Reinhart, A., & Scott, J. G. (2018). Sequential nonparametric tests for a change in distribution: An application to detecting radiological anomalies. Journal of the American Statistical Association. doi:10.1080/01621459.2018.1476245. http://arxiv.org/abs/1612.07867
- Tansey, W., Athey, A., Reinhart, A., & Scott, J. G. (2017). Multiscale spatial density smoothing: An application to large-scale radiological survey and anomaly detection. Journal of the American Statistical Association, 112(519), 1047–1063. doi:10.1080/01621459.2016.1276461. http://arxiv.org/abs/1507.07271
- Reinhart, A., Ventura, V., & Athey, A. (2015). Detecting changes in maps of gamma spectra with Kolmogorov–Smirnov tests. Nuclear Instruments and Methods in Physics Research A, 802, 31–37. doi:10.1016/j.nima.2015.09.002. http://arxiv.org/abs/1507.06954
- Reinhart, A., Athey, A., & Biegalski, S. (2014). Spatially-Aware Temporal Anomaly Mapping of Gamma Spectra. IEEE Transactions on Nuclear Science, 61(3), 1284–1289. doi:10.1109/TNS.2014.2317593. http://arxiv.org/abs/1405.1135
- Reinhart, A. (2013, April). An Integrated System for Gamma-Ray Spectral Mapping and Anomaly Detection (Undergraduate thesis). University of Texas at Austin. https://hdl.handle.net/2152/20071
I have an active interest in statistical pedagogy, and in developing new ways to improve student learning, assess understanding of statistical concepts, and better teach the foundations of statistical reasoning. I help lead the Teaching Statistics Group at Carnegie Mellon University’s Department of Statistics & Data Science.
- P Burckhardt, P W Elliott, C Evans, S Hyun, K Lin, A Luby, C P Makris, M Meyer, J Orellana, R Yurko, G Weinberg, J Wieczorek, R Nugent & A Reinhart (2018). Developing an assessment for concepts in introductory statistics and data science. CMU Eberly Teaching and Learning Summit. (People’s Choice Award winner)
- S Hyun, P Burckhardt, P Elliott, C Evans, K Lin, A Luby, C P Makris, J Orellana, A Reinhart, J Wieczorek, R Yurko, G Weinberg, & R Nugent (2018). Identifying misconceptions of introductory data science using a think-aloud protocol, eCOTS 2018.
- Burckhardt P, Elliott P, Hyun S, Lin K, Luby A, Makris CP, Orellana J, Reinhart A, Wieczorek J, Weinberg G, Nugent R (2017). Assessment of Student Learning and Misconception Identification in Intro Statistics, CMU Eberly Teaching and Learning Summit.